This PR introduces a way to implement Attribute Based Access Control (ABAC) for the Llama Stack server. The rough design is: - https://github.com/meta-llama/llama-stack/pull/1626 added a way for the Llama Stack server to query an authenticator - We build upon that and expect "access attributes" as part of the response. These attributes indicate the scopes available for the request. - We use these attributes to perform access control for registered resources as well as for constructing the default access control policies for newly created resources. - By default, if you support authentication but don't return access attributes, we will add a unique namespace pointing to the API_KEY. That way, all resources by default will be scoped to API_KEYs. An important aspect of this design is that Llama Stack stays out of the business of credential management or the CRUD for attributes. How you manage your namespaces or projects is entirely up to you. The design only implements access control checks for the metadata / book-keeping information that the Stack tracks. ### Limitations - Currently, read vs. write vs. admin permissions aren't made explicit, but this can be easily extended by adding appropriate attributes to the `AccessAttributes` data structure. - This design does not apply to agent instances since they are not considered resources the Stack knows about. Agent instances are completely within the scope of the Agents API provider. ### Test Plan Added unit tests, existing integration tests |
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Llama Stack
Quick Start | Documentation | Colab Notebook
Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides
- Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
- Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
- Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
- Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
- Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
Llama Stack Benefits
- Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
- Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
- Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications.
API Providers
Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack.
API Provider Builder | Environments | Agents | Inference | Memory | Safety | Telemetry |
---|---|---|---|---|---|---|
Meta Reference | Single Node | ✅ | ✅ | ✅ | ✅ | ✅ |
SambaNova | Hosted | ✅ | ||||
Cerebras | Hosted | ✅ | ||||
Fireworks | Hosted | ✅ | ✅ | ✅ | ||
AWS Bedrock | Hosted | ✅ | ✅ | |||
Together | Hosted | ✅ | ✅ | ✅ | ||
Groq | Hosted | ✅ | ||||
Ollama | Single Node | ✅ | ||||
TGI | Hosted and Single Node | ✅ | ||||
NVIDIA NIM | Hosted and Single Node | ✅ | ||||
Chroma | Single Node | ✅ | ||||
PG Vector | Single Node | ✅ | ||||
PyTorch ExecuTorch | On-device iOS | ✅ | ✅ | |||
vLLM | Hosted and Single Node | ✅ | ||||
OpenAI | Hosted | ✅ | ||||
Anthropic | Hosted | ✅ | ||||
Gemini | Hosted | ✅ |
Distributions
A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider implementations for each API component. Distributions make it easy to get started with a specific deployment scenario - you can begin with a local development setup (eg. ollama) and seamlessly transition to production (eg. Fireworks) without changing your application code. Here are some of the distributions we support:
Distribution | Llama Stack Docker | Start This Distribution |
---|---|---|
Meta Reference | llamastack/distribution-meta-reference-gpu | Guide |
Meta Reference Quantized | llamastack/distribution-meta-reference-quantized-gpu | Guide |
SambaNova | llamastack/distribution-sambanova | Guide |
Cerebras | llamastack/distribution-cerebras | Guide |
Ollama | llamastack/distribution-ollama | Guide |
TGI | llamastack/distribution-tgi | Guide |
Together | llamastack/distribution-together | Guide |
Fireworks | llamastack/distribution-fireworks | Guide |
vLLM | llamastack/distribution-remote-vllm | Guide |
Documentation
Please checkout our Documentation page for more details.
- CLI references
- llama (server-side) CLI Reference: Guide for using the
llama
CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. - llama (client-side) CLI Reference: Guide for using the
llama-stack-client
CLI, which allows you to query information about the distribution.
- llama (server-side) CLI Reference: Guide for using the
- Getting Started
- Quick guide to start a Llama Stack server.
- Jupyter notebook to walk-through how to use simple text and vision inference llama_stack_client APIs
- The complete Llama Stack lesson Colab notebook of the new Llama 3.2 course on Deeplearning.ai.
- A Zero-to-Hero Guide that guide you through all the key components of llama stack with code samples.
- Contributing
- Adding a new API Provider to walk-through how to add a new API provider.
Llama Stack Client SDKs
Language | Client SDK | Package |
---|---|---|
Python | llama-stack-client-python | |
Swift | llama-stack-client-swift | |
Typescript | llama-stack-client-typescript | |
Kotlin | llama-stack-client-kotlin |
Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from python, typescript, swift, and kotlin programming languages to quickly build your applications.
You can find more example scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo.